Design of Experiments: Learning More with Fewer Trials
Use factorial thinking, randomisation and interaction analysis to improve materials and processes efficiently.
Why one-factor-at-a-time fails
Changing one variable while holding others fixed misses interactions and often uses trials inefficiently.
Factorial designs
Structured combinations estimate main effects and interactions. Fractional designs reduce runs when full factorials are impractical.
Randomisation and blocking
Randomisation protects against time-related bias; blocking separates known nuisance variation.
From significance to usefulness
Statistical significance does not guarantee engineering importance. Effect size, uncertainty and reproducibility remain central.
Engineering takeaway
Define the response, controllable factors, noise factors and decision threshold before running experiments.